Geology topics

Lisa M. Holsinger

Publications and source records attributed to Lisa M. Holsinger.

11 recordsLinked to original sources

USGS2022

Modeled interactions of mountain pine beetle and wildland fire under future climate and management scenarios for three western US landscapes

Mountain pine beetle (MPB) is a native disturbance agent across most pine forests in the western US. Climate changes will directly and indirectly impact frequencies and severities of MPB outbreaks, which can then alter fuel characteristics and wildland fire dynamics via changes in stand structure and composition. To investigate the importance of MPB to past and future landscape dynamics, we used the mechanistic, spatially explicit ecosystem process model FireBGCv2 to quantify interactions among climate, MPB, wildfire, fire suppression, and fuel management under historical and projected future climates for three western US landscapes. We compared simulated FireBGCv2 output from three MPB modules (none, simple empirical, and complex mechanistic) using three focus variables and six exploratory variables to evaluate the importance of MPB to landscape dynamics.

Fire Ecology↗

USGS2021

Improved fire severity mapping in the North American boreal forest using a hybrid composite method

Fire severity is a key driver shaping the ecological structure and function of North American boreal ecosystems, a biome dominated by large, high-intensity wildfires. Satellite-derived burn severity maps have been an important tool in these remote landscapes for both fire and resource management. The conventional methodology to produce satellite-inferred fire severity maps generally involves comparing imagery from 1 year before and 1 year after a fire, yet environmental conditions unique to the boreal have limited the accuracy of resulting products. We introduce an alternative method – the ‘hybrid composite’ – based on deriving mean severity over time on a per-pixel basis within the cloud-computing environment of Google Earth Engine. It constructs the post-fire image from satellite data composited from all valid images (i.e., clear-sky and snow-free) acquired in the time period immediately after fire through the early growing season of the following year. We compare this approach to paired-scene and composite approaches where the post-fire time period is from the growing season 1 year after fire. Validation statistics based on field-derived data for 52 fires across Alaska and Canada indicate that the hybrid composite method outperforms the other approaches. This approach presents an efficient and cost-effective means to monitor and explore trends and patterns across broad spatial domains, and could be applied to fires in other regions, especially those with frequent cloud cover or rapid vegetation recovery.

Alaska↗

USGS2020

Simulation modeling of complex climate, wildfire, and vegetation dynamics to address wicked problems in land management

Complex, reciprocal interactions among climate, disturbance, and vegetation dramatically alter spatial landscape patterns and influence ecosystem dynamics. As climate and disturbance regimes shift, historical analogs and past empirical studies may not be entirely appropriate as templates for future management. The need for a better understanding of the potential impacts of climate changes on ecosystems is reaching a new level of urgency, especially in highly perturbed or vulnerable ecological systems. Simulation models are extremely useful tools for guiding management decisions in an era of rapid change, thus providing potential solutions for wicked problems in land management—those that are difficult to solve and inherently resistant to easily definable solutions. We identify three experimental approaches for landscape modeling that address management challenges in the context of uncertain climate futures and complex ecological interactions: (1) an historical comparative approach, (2) a future comparative approach, and (3) threshold detection. We provide examples of each approach from previously published studies of simulated climate, disturbance, and landscape dynamics in forested landscapes of the western United States, modeled with the FireBGCv2 ecosystem process model. Cumulatively, model outcomes indicate that typical land management strategies will likely not be sufficient to counteract the impacts of rapid climate change and altered disturbance regimes that threaten the stability of ecosystems. Without implementation of new, adaptive management strategies, future landscapes are very likely to be different than historical or contemporary ones, with significant and sometimes persistent changes triggered by interactions of climate and wildfire.

Montana, New Mexico, Wyoming↗

USGS2020

Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates

Land managers need new tools for planning novel futures due to climate change. Species distribution modeling (SDM) has been used extensively to predict future distributions of species under different climates, but their map products are often too coarse for fine-scale operational use. In this study we developed a flexible, efficient, and robust method for mapping current and future distributions and abundances of vegetation species and communities at the fine spatial resolutions that are germane to land management. First, we mapped Potential Vegetation Types (PVTs) using conventional statistical modeling techniques (Random Forests) that used bioclimatic ecosystem process and climate variables as predictors. We obtained over 50% accuracy across 13 mapped PVTs for our study area. We then applied future climate projections as climate input to the Random Forest model to generate future PVT maps, and used field data describing the occurrence of tree and non-tree species in each PVT category to model and map species distribution for current and future climate. These maps were then compared to two previous SDM mapping efforts with over 80% agreement and equivalent accuracy. Because PVTs represent the biophysical potential of the landscape to support vegetation communities as opposed to the vegetation that currently exists, they can be readily linked to climate forecasts and correlated with other, climate-sensitive ecological processes significant in land management, such as fire regimes and site productivity.

Montana↗

USGS2019

Giving ecological meaning to satellite-derived fire severity metrics across North American forests

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R 2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.

Remote Sensing↗

USGS2019

Selecting a landscape model for natural resource management applications

Purpose of Review: Climate change and associated ecological impacts have challenged many conventional, observation-based approaches for predicting ecosystem and landscape responses to natural resource management. Complex spatial ecological models provide powerful, flexible tools which managers and others can use to make inferences about management impacts on future, no-analog landscape conditions. However, land managers who wish to use ecosystem and landscape models for natural resource applications are faced with the difficult task of deciding among many models that differ in important ways. Here, we summarize a process to aid managers in the selection of an appropriate model for natural resource management. Recent Findings: To guide management planning, scientifically credible information on how landscapes will respond to management actions under changing climate is required. Landscape models are increasingly used in a management context to evaluate of impacts of changing climate and interacting stressors on ecosystems and to test effects of alternative management options on desired conditions. However, the wide range of available models makes selection of appropriate and viable models a complex process. Summary: We present a series of preliminary steps to define critical scales of time, space, and ecological organization to guide an experimental design for a modeling project and then list a set of criteria for selecting a landscape or ecological model. Material presented includes the preliminary steps (crafting modeling objective, designing modeling project), organizational concerns (resources available, expertise on hand, timelines), and modeling details (complexity, design, documentation) of model selection.

Current Landscape Ecology Reports↗

USGS2018

Use of landscape simulation modeling to quantify resilience for ecological applications

Goals of fostering ecological resilience are increasingly used to guide U.S. public land management in the context of anthropogenic climate change and increasing landscape disturbances. There are, however, few operational means of assessing the resilience of a landscape or ecosystem. We present a method to evaluate resilience using simulation modeling. In this method, we use historical conditions (e.g., in North America, prior to European settlement), quantified using simulation modeling, to provide a comparative reference for contemporary conditions, where substantial departures indicate loss of resilience. Contemporary ecological conditions are compared statistically to the historical time series to create a resilience index, which can be used to prioritize landscapes for treatment and inform possible treatments. However, managing for resilience based on historical conditions is tenuous in the Anthropocene, which is characterized by rapid climate change, extensive human land use, altered disturbance regimes, and exotic species introductions. To account for the future variability of ecosystems resulting from climate and disturbance regime shifts, we augment historical simulations with simulations of ecosystem dynamics under projected climate and land use changes to assess the degree of departure from benchmark historical conditions. We use a mechanistic landscape model (FireBGCv2) applied to a large landscape in western Montana, USA, to illustrate the methods presented in this paper. Spatially explicit ecosystem modeling provides the vehicle to generate the historical and future time series needed to quantify potential resilience conditions associated with past and potential future conditions. Our methods show that given selection of a useful set of metrics, managers could use simulations like ours to evaluate potential future management directions.

Ecosphere↗

USGS2018

Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential

Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre-and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: the delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection and instead use a mean compositing approach in which all valid pixels (e.g. cloud-free) over a pre-specified date range (pre- and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared the standard approach in which one pre-fire and post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western US. These validations are compared to Landsat-based fire severity datasets produced using only one pre- and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets show improved validation statistics compared to parallel versions in which only one pre-fire and post-fire scene are used. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e. fire perimeters) be produced independently and prior to applying our methods, we suggest our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.

Remote Sensing↗

USGS2018

Effects of climate change on forest vegetation in the Northern Rockies Region

The projected rapid changes in climate will affect the unique vegetation assemblages of the Northern Rockies region in myriad ways, both directly through shifts in vegetation growth, mortality, and regeneration, and indirectly through changes in disturbance regimes and interactions with changes in other ecosystem processes, such as hydrology, snow dynamics, and exotic invasions (Bonan 2008; Hansen and Phillips 2015; Hansen et al. 2001; Notaro et al. 2007). These impacts, taken collectively, could change the way vegetation is managed by public land agencies in this area. Some species may be in danger of rapid decreases in abundance, while others may undergo range expansion (Landhäusser et al. 2010). New vegetation communities may form, while historical vegetation complexes may simply shift to other areas of the landscape or become rare. Juxtaposed with climate change concerns are the consequences of other land management policies and past activities, such as fire exclusion, fuels treatments, and grazing. A thorough assessment of the responses of vegetation to projected climate change is needed, along with an evaluation of the vulnerability of important species, communities, and vegetation-related resources that may be influenced by the effects, both direct and indirect, of climate change. This assessment must also account for past management actions and current vegetation conditions and their interactions with future climates.

General Technical Report↗

USGS2017

Effects of climate change on forest vegetation in the northern Rockies

Increasing air temperature, through its influence on soil moisture, is expected to cause gradual changes in the abundance and distribution of tree, shrub, and grass species throughout the Northern Rockies, with drought tolerant species becoming more competitive. The earliest changes will be at ecotones between lifeforms (e.g., upper and lower treelines). Ecological disturbance, including wildfire and insect outbreaks, will be the primary facilitator of vegetation change, and future forest landscapes may be dominated by younger age classes and smaller trees. High-elevation forests will be especially vulnerable if disturbance frequency

Rocky Mountains↗

USGS2016

Interactions of landscape disturbances and climate change dictate ecological pattern and process: spatial modeling of wildfire, insect, and disease dynamics under future climates

Context Interactions among disturbances, climate, and vegetation influence landscape patterns and ecosystem processes. Climate changes, exotic invasions, beetle outbreaks, altered fire regimes, and human activities may interact to produce landscapes that appear and function beyond historical analogs. Objectives We used the mechanistic ecosystem-fire process model FireBGCv2 to model interactions of wildland fire, mountain pine beetle ( Dendroctonus ponderosae ), and white pine blister rust ( Cronartium ribicola ) under current and future climates, across three diverse study areas. Methods We assessed changes in tree basal area as a measure of landscape response over a 300-year simulation period for the Crown of the Continent in north-central Montana, East Fork of the Bitterroot River in western Montana, and Yellowstone Central Plateau in western Wyoming, USA. Results Interacting disturbances reduced overall basal area via increased tree mortality of host species. Wildfire decreased basal area more than beetles or rust, and disturbance interactions modeled under future climate significantly altered landscape basal area as compared with no-disturbance and current climate scenarios. Responses varied among landscapes depending on species composition, sensitivity to fire, and pathogen and beetle suitability and susceptibility. Conclusions Understanding disturbance interactions is critical for managing landscapes because forest responses to wildfires, pathogens, and beetle attacks may offset or exacerbate climate influences, with consequences for wildlife, carbon, and biodiversity.

Landscape Ecology↗